Pathology Detection in head MRI Scans with Deep Learning

Our goal in this project is to create a tool for detecting anomalies in MRI Head scan images. Reading MRI scan with a naked eye is a complicated task that requires a lot of experience. Hence, we would like to create a tool to help radiologists with this task, by alerting them of abnormality.
Alerting of anomalies is made possible by learning the distribution of healthy patients’ MRI Head scans. Given the distribution, it is possible to check the likelihood of a scan – that of an unhealthy patient is expected to be unlikely. Learning distributions of such high dimensionality, was considered an almost impossible task – “The curse of dimensionality”. A recent breakthrough in the field of generative models makes it possible, at least theoretically.
After learning MRI’s distribution, we used the model for creating a tool for detecting anomalies: We presented the results with a heat map, that highlights the areas on the scan that were found as less likely. Based on our results, we believe that a tool to detect anomalies can be created.